scholarly journals Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

2021 ◽  
Author(s):  
Tomas Havranek ◽  
Roman Horvath ◽  
Ali Elminejad
Keyword(s):  
Labor Supply ◽  
Publication Bias ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Extensive Margin ◽  
Real Business Cycle ◽  
Intertemporal Substitution ◽  
The Mean ◽  
Frisch Elasticity ◽  
Frequentist Model Averaging

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

scholarly journals Model Averaging and Its Use in Economics

2020 ◽  
Vol 58 (3) ◽  
pp. 644-719 ◽  
Author(s):  
Mark F. J. Steel
Keyword(s):  
Model Uncertainty ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Linear Regression Models ◽  
Bayesian Procedures ◽  
Wide Range ◽  
Computational Resources ◽  
Production Modeling ◽  
Frequentist Model Averaging

The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical techniques to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas including growth economics, production modeling, finance and forecasting macroeconomic quantities. (JEL C11, C15, C20, C52, O47).

Model Uncertainty and Model Averaging in Regression Discontinuity Designs

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Patrick Button
Keyword(s):  
Model Uncertainty ◽  
Regression Discontinuity ◽  
Model Averaging ◽  
Parametric Model ◽  
Parametric Models ◽  
Standard Errors ◽  
Randomized Experiments ◽  
Monte Carlo Experiments ◽  
Regression Discontinuity Designs ◽  
Frequentist Model Averaging

AbstractParametric (polynomial) models are popular in research employing regression discontinuity designs and are required when data are discrete. However, researchers often choose a parametric model based on data inspection or pretesting. These approaches lead to standard errors and confidence intervals that are too small because they do not incorporate model uncertainty. I propose using Frequentist model averaging to incorporate model uncertainty into parametric models. My Monte Carlo experiments show that Frequentist model averaging leads to mean square error and coverage probability improvements over pretesting. An application to [Lee, D. S. 2008. “Randomized Experiments From Non-Random Selection in US House Elections.”

scholarly journals Monopsony in Labor Markets: A Meta-Analysis

ILR Review ◽  
2020 ◽  
Vol 74 (1) ◽  
pp. 27-55 ◽  
Author(s):  
Anna Sokolova ◽  
Todd Sorensen
Keyword(s):  
Labor Supply ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Model Averaging ◽  
Labor Market Institutions ◽  
Selective Reporting ◽  
Wage Setting ◽  
The Mean ◽  
Monopsony Power ◽  
Identification Strategy

When jobs offered by different employers are not perfect substitutes, employers gain wage-setting power; the extent of this power can be captured by the elasticity of labor supply to the firm. The authors collect 1,320 estimates of this parameter from 53 studies. Findings show a prominent discrepancy between estimates of direct elasticity of labor supply to changes in wage (smaller) and the estimates converted from inverse elasticities (larger), suggesting that labor market institutions may rein in a substantial amount of firm wage-setting power. This gap remains after they control for 22 additional variables and use Bayesian Model Averaging and LASSO to address model uncertainty; however, it is less pronounced for studies employing an identification strategy. Furthermore, the authors find strong evidence that implies the literature on direct estimates is prone to selective reporting: Negative estimates of the elasticity of labor supply to the firm tend to be discarded, leading to upward bias in the mean reported estimate. Additionally, they point out several socioeconomic factors that seem to affect the degree of monopsony power.

scholarly journals Optimal designs for frequentist model averaging

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 665-682
Author(s):  
K Alhorn ◽  
K Schorning ◽  
H Dette
Keyword(s):  
Mean Squared Error ◽  
Model Averaging ◽  
Optimal Solution ◽  
Regression Function ◽  
Optimal Designs ◽  
Squared Error ◽  
Bayesian Optimal Design ◽  
The Mean ◽  
Asymptotic Mean Squared Error ◽  
Frequentist Model Averaging

SummaryWe consider the problem of designing experiments for estimating a target parameter in regression analysis when there is uncertainty about the parametric form of the regression function. A new optimality criterion is proposed that chooses the experimental design to minimize the asymptotic mean squared error of the frequentist model averaging estimate. Necessary conditions for the optimal solution of a locally and Bayesian optimal design problem are established. The results are illustrated in several examples, and it is demonstrated that Bayesian optimal designs can yield a reduction of the mean squared error of the model averaging estimator by up to 45%.

scholarly journals Death to the Cobb-Douglas Production Function? A Meta-Analysis of the Capital-Labor Substitution Elasticity

2019 ◽  
Author(s):  
Tomas Havranek ◽  
Zuzana Irsova ◽  
Sebastian Gechert ◽  
Dominika Kolcunova
Keyword(s):  
Publication Bias ◽  
Production Function ◽  
Meta Analysis ◽  
Model Averaging ◽  
Weight Of Evidence ◽  
Empirical Literature ◽  
Order Condition ◽  
Aggregated Data ◽  
First Order ◽  
The Mean

We show that the large elasticity of substitution between capital and labor estimated in the literature on average, 0.9, can be explained by three factors: publication bias, use of aggregated data, and omission of the first-order condition for capital. The mean elasticity conditional on the absence of publication bias, disaggregated data, and inclusion of information from the first-order condition for capital is 0.3. To obtain this result, we collect 3,186 estimates of the elasticity reported in 121 studies, codify 71 variables that reflect the context in which researchers produce their estimates, and address model uncertainty by Bayesian and frequentist model averaging. We employ nonlinear techniques to correct for publication bias, which is responsible for at least half of the overall reduction in the mean elasticity from 0.9 to 0.3. Our findings also suggest that a failure to normalize the production function leads to a substantial upward bias in the estimated elasticity. The weight of evidence accumulated in the empirical literature emphatically rejects the Cobb-Douglas specification.

scholarly journals Student Employment and Education: A Meta-Analysis

2021 ◽  
Author(s):  
Katerina Kroupova ◽  
Tomas Havranek ◽  
Zuzana Irsova
Keyword(s):  
Young People ◽  
Publication Bias ◽  
Educational Outcomes ◽  
High Intensity ◽  
Meta Analysis ◽  
Model Averaging ◽  
Student Employment ◽  
Frequentist Model Averaging

Educational outcomes have many determinants, but one that most young people can readily control is choosing whether to work while in school. Sixty-nine studies have estimated the effect, but results vary from large negative to positive estimates. We show that the results are systematically driven by context, publication bias, and treatment of endogeneity. Studies ignoring endogeneity suffer from an upward bias, which is almost fully compensated by publication selection in favor of negative estimates. Net of the biases, the literature suggests a negative but economically inconsequential mean effect. The effect is more negative for high-intensity employment and educational outcomes measured as decisions to dropout, but it is positive in Germany. To derive these results we collect 861 previously reported estimates together with 32 variables reflecting estimation context, use recently developed nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to assign a pattern to the heterogeneity in the literature.

scholarly journals Frequentist model averaging for threshold models

2018 ◽  
Vol 71 (2) ◽  
pp. 275-306 ◽  
Author(s):  
Yan Gao ◽  
Xinyu Zhang ◽  
Shouyang Wang ◽  
Terence Tai-leung Chong ◽  
Guohua Zou
Keyword(s):  
Model Averaging ◽  
Threshold Models ◽  
Frequentist Model Averaging

scholarly journals The Antihypertensive Effects and Safety of LCZ696 in Patients with Hypertension: A Systemic Review and Meta-Analysis of Randomized Controlled Trials

2021 ◽  
Vol 10 (13) ◽  
pp. 2824
Author(s):  
Su-Kiat Chua ◽  
Wei-Ting Lai ◽  
Lung-Ching Chen ◽  
Huei-Fong Hung
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Randomized Controlled Trials ◽  
Diastolic Blood Pressure ◽  
Publication Bias ◽  
Meta Analysis ◽  
Systemic Review ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
The Mean

Background: The management of hypertension remains suboptimal throughout the world. Methods: We performed a random-effects model meta-analysis of randomized controlled trials to determine the effectiveness and safety of sacubitril/valsartan (LCZ696) for the treatment of high arterial pressure. Relevant published articles from PubMed, Cochrane base, and Medline were examined, and the last search date was December 2020. Only published randomized controlled trials and double-blind studies were selected for further analysis. The mean reductions in systolic blood pressure (msSBP) and diastolic blood pressure (msDBP) in the sitting position, as well as the mean reductions in ambulatory systolic blood pressure (maSBP) and ambulatory diastolic blood pressure (maDBP), were assumed as efficacy endpoints. Adverse events (AEs) were considered as safety outcomes. Results: Ten studies with a total of 5931patients were included for analysis. Compared with placebo, LCZ696 had a significant reduction in msSBP (weight mean difference (WMD) = −6.52 mmHg, 95% confidence interval (CI): −8.57 to −4.47; p < 0.001), msDBP (WMD = −3.32 mmHg, 95% CI: −4.57 to −2.07; p < 0.001), maSBP (WMD = −7.08 mmHg, 95% CI: −10.48 to −3.68; p < 0.001), maDBP (WMD = −3.28 mmHg, 95% CI: −4.55 to −2.02, p < 0.001). In subgroup analysis, only 200 mg and 400 mg LCZ696 showed a significant BP reduction. There was no difference in the AE rate between the LCZ696 and placebo groups (WMD = 1.02, 95% CI: 0.83 to 1.27, p = 0.54). Egger’s test revealed a potential publication bias for msSBP (p = 0.025), but no publication bias for other outcomes. Conclusion: LCZ696 may reduce blood pressure more efficaciously than traditional therapy in hypertensive patients without increasing adverse effects.

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